• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Iranian Conference on Biomedical Engineering >Bidirectional neural network for pathological voice detection
【24h】

Bidirectional neural network for pathological voice detection

机译：用于病理语音检测的双向神经网络

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

We showed in our recent work that Bidirectional neural network (BNN) is a powerful tool for feature compensation in automatic speech recognition systems. In this paper, we have introduced BNN as feature compensator for better discriminating of pathological voices from normal subjects. Mel-Frequency Cepstral Coefficients (MFCCs) were extracted from each frame of sample voices and were compensated in two steps. First, BNN is trained with both normal and pathological feature vectors. Our hypothesis is that BNN can extract useful knowledge about the patterns of each class during training step. In second step, MFCC feature vectors feed into BNN and compensate according to latent knowledge of BNN. In the last step, Compensated MFCCs are classified as pathological or normal by HMMs. We achieved 4.67%, 2.81% and 2.24% improvement in measures of specificity, accuracy and sensitivity by compensated feature vectors compared to the original feature vectors. Results corroborated our hypothesis about the ability of BNN in compensation of feature vectors in a way that these features become more suitable for detection of pathological voices from normal ones.
机译：我们在我们最近的工作中显示了双向神经网络（BNN）是一种强大的自动语音识别系统功能补偿的工具。在本文中，我们推出了BNN作为特征补偿器，以便更好地区分来自普通科目的病理声音。从每一帧样品型样品框中提取熔融频率抗肌射潮系数（MFCC），并以两个步骤得到补偿。首先，BNN培训，具有正常和病理特征向量。我们的假设是，BNN可以在训练步骤期间提取关于每个类别的模式的有用了解。在第二步中，MFCC特征向量进入BNN并根据BNN的潜在知识进行补偿。在最后一步中，补偿的MFCC被HMMS归类为病理或正常。与原始特征向量相比，我们通过补偿特征向量实现了4.67％，更高的特异性，准确性和灵敏度的措施2.81％和2.24％。结果证明了我们关于BNN在特征向量补偿中的能力的假设，以至于这些特征变得更适合于从正常情况下检测病理声音的方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号